Decoding Data & AI: How Recommendation Engines Work
Recommendations: essential in today's information overflow
Whether it's shopping on Amazon to fill up our groceries in the morning, listening to songs on Spotify during the commute to work, or watching a movie on our favorite streaming service in the evening, we as consumers love to take full advantage of the vast options the digital world offers us. However, according to Statista, Spotify offers over 100 million songs, while Amazon has a staggering 488 million products. This immense variety can make it difficult for consumers to locate what they want.
To make this easier, online retailers and service providers use personalized content to enhance the customer experience and - in the end - increase conversions. This personalization is mainly made possible by recommendation engines.
Suggestions for a new song, product, or movie from those systems not only improve customer satisfaction but obviously also lead to increased sales for companies. Today, for example, more than 80 percent of the TV shows people watch on Netflix are discovered through the platform’s recommendation system (Wired). But how do recommendation systems actually work? Below, we will delve into the two most popular approaches: the collaborative filtering approach and the content-based approach.
The Collaborative Approach: simple and straightforward
Do sometimes ask a friend for movie recommendations, because you know this friend has a similar taste to yours? The collaborative approach works in the same way. By comparing your historical consumption and rating behavior with that of the entire user base, the system identifies "statistical twins" who share similar preferences and interests. These similarities are then used to make personalized predictions for you. This concept can be illustrated with the following matrix.
The rows of the matrix represent the users Eva, Anatoli, and Felix, while the columns represent movies. In the cells, we see the rating given to the movie by the respective users from 1 to 5 stars. the movie was highly enjoyed. Similar to the 5-star rating that we know from Amazon. Additionally, there are empty cells indicating that a user has not yet seen or rated the movie, as is very likely. So the question is: should we recommend "The Great Gatsby" to Eva?
The collaborative approach first assesses the similarity between Eva and other users who have already rated "The Great Gatsby." For instance, in our table, we see that Anatoli seems to have similar taste to Eva, as they rate movies very similarly. Anatoli enjoyed "The Great Gatsby" - so in this case we would recommend it to her!
This is only a simple example, but with hundreds of millions of users and movies or products, a massive matrix is generated. To extract recommendations from such an extensive matrix, various mathematical techniques, such as matrix factorization, are employed to manage and process the enormous amount of data efficiently.
While this requires a lot of processing power, still this approach is very similar to finding a friend who has similar tastes to you, only you will probably never know them and Netflix or Amazon are finding them for you.
The Content-based approach: complex, but with great application potential
The content-based approach on the other hand, tries to break down a product or movie into its key characteristics: these attributes might include the genre, director, or actors of a movie (or indeed many more). In a second step, we look at which characteristics of a movie make it likely that you will enjoy it. Do you like action, do you like a specific actor, do you like urban locations, open endings, or what have you - the list can be very long.
For example, if we consider our user Eva, the content-based approach would create a user profile based on the attributes of the products from her purchase and rating history, similar to the following matrix.
When a new movie like "The Great Gatsby" is released from the streaming provider, classification algorithms can use the relationships between attributes and rating labels, and estimate how Eva would rate this movie.
Both the collaborative and content-based approaches are among the most popular methods for recommendation systems, valued for their ease of use and performance. The content-based model could even be used to algorithmically help with product or movie development. If we know which features of a movie will lead to higher ratings across the entire user base, we know pretty precisely what users like and can produce new movies accordingly.
In practice, recommendation systems often encounter the so-called "Cold Start Problem." This issue arises when the system does not yet have enough data for a user to make accurate recommendations. To mitigate this, providers frequently ask for personal preferences during the registration process. By gathering information about a user's likes and dislikes upfront, the system can offer recommendations even before the user interacts with any content, such as a song or a movie.
As the user continues to use the system, it learns more about their preferences and gradually improves the accuracy and relevance of its recommendations. This ongoing learning process ensures that the recommendations become increasingly personalized and effective over time.
What does this mean for you?
If your business has a certain number of customer touchpoints, you should consider a recommendation system for your webshop or your online marketing. Now that you understand how they work, you know how your customers' interactions can be optimized using existing data. With the help of ratings and purchases, not only cross-selling and up-selling potential can be opened up, but the product range can also be tailored to individual customer preferences.
Want to learn more about OMMAX's expertise in data & AI? Get in touch with our experts through the form below, and sign up for our Decoding Data & AI series!
Contact an expert
Do you want to know more about our expertise? Get in touch!
Sign Up for the Newsletter
Development and Execution of a Customized Digital Growth Strategy